nonrecurrent

Nonrecurrent is an adjective used in several disciplines to describe entities that do not exhibit recurrence or repeated visitation. In dynamical systems and topology, a point or orbit is nonrecurrent if it does not return arbitrarily close to its initial position under iteration; this contrasts with recurrent points whose orbit returns near the starting point infinitely often. Nonrecurrent behavior is studied in ergodic theory and chaos theory to understand long-term behavior of systems.

Within machine learning and neural networks, nonrecurrent models are those that process each input independently and do not maintain a hidden state across time. They contrast with recurrent neural networks (RNNs) and their variants, where information persists through time steps. Nonrecurrent architectures include feedforward neural networks, convolutional neural networks, and many transformer-based models. The term emphasizes the absence of temporal recurrence, not necessarily the absence of depth or complexity. These models typically rely on fixed-size input representations and backpropagation through layers rather than backpropagation through time.

Applications for nonrecurrent models span image and speech recognition with CNNs, natural language processing with transformers, and modeling of tabular data with multilayer perceptrons. In sequence modeling, nonrecurrent approaches can use sliding windows or positional encodings to capture temporal structure without recurrence.